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As algorithmic risk assessment instruments (RAIs) are increasingly adopted toassist decision makers, their predictive performance and potential to promoteinequity have come under scrutiny. However, while most studies examine thesetools in isolation, researchers have come to recognize that assessing theirimpact requires understanding the behavior of their human interactants. In thispaper, building off of several recent crowdsourcing works focused on criminaljustice, we conduct a vignette study in which laypersons are tasked withpredicting future re-arrests. Our key findings are as follows: (1) Participantsoften predict that an offender will be rearrested even when they deem thelikelihood of re-arrest to be well below 50%; (2) Participants do not anchor onthe RAI's predictions; (3) The time spent on the survey varies widely acrossparticipants and most cases are assessed in less than 10 seconds; (4) Judicialdecisions, unlike participants' predictions, depend in part on factors that areorthogonal to the likelihood of re-arrest. These results highlight theinfluence of several crucial but often overlooked design decisions and concernsaround generalizability when constructing crowdsourcing studies to analyze theimpacts of RAIs.

The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies